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It is worth studying that how to utilize image features so as to achieve the satisfied result of CBIR (content-based image retrieval). To solve this problem, this paper proposes an adaptive image retrieval algorithm based on color feature and texture feature, in which the two kinds of features are combined and the weight coefficients of them are determined with genetic algorithm. Genetic algorithm, starting from solving practical problems, constructs an initial population with the potential solutions of practical problems. In the proposed algorithm, firstly, the initial population consisted with the weight coefficients of texture features (or color ones) is randomly generated; then, selection, crossover, and mutation are operated so that each new generation of population is gradually closed to the optimal solution; finally, the adaptively adjusted weight coefficients are obtained. Experimental results show that when fusing color feature and texture feature in image retrieval, the introduction of genetic algorithm, by which determine the weight coefficients of two kinds of features, makes both the recall ration and precision ratio of retrieval are improved. The proposed algorithm of image retrieval combined with genetic algorithm can automatically set the optimal weights of the image features according to the different image to be retrieved submitted by users, and can basically achieve the ideal suitable weights and output the relative ideal retrieval results. Introduction With changes in application requirements, how to retrieve the special image from the massive image database, is becoming more and more important. Traditional TBIR (text based image retrieval) technology has many deficiencies, and CBIR (content-based image retrieval) using visual characteristics of the image itself can accurately find the desired image from a number of image library with high-efficient. So CBIR has become the hotspot [1]. CBIR mainly utilizes the distance measure function, which is calculated by the visual features or semantic features extracted from the detected image and the images in the database, to match the similarity. Finally, the retrieval results will be given to the user in accordance with the order of the similarity. Image features include color feature, texture feature, shape feature and so on, and color feature is one of the essential visual information. In all the underlying visual features of image, color is the most significant and stable visual one compared to the other features, such as texture, shape contour and so on. Scholars at home and abroad have carried out extensive research on the retrieval algorithm based on the underlying feature of the image. Reference [2] defines a color similarity metric, and adopted it in CBIR; [3] proposed a concept of weighted color histogram based on color contrast of multi-scale neighborhood. 6th International Conference on Information Engineering for Mechanics and Materials (ICIMM 2016) © 2016. The authors Published by Atlantis Press 221 Texture feature is another important visual feature of image. An algorithm of image retrieval based on texture feature is proposed in [4]. In recent years, many researchers have noticed that CBIR cannot simply uses the color, texture, such as a feature of CBIR often unable to obtain good retrieval effectiveness and to the comprehensive utilization of the color feature and texture characteristics of image features for CBIR of extensive research and discussion [5-8] However, how to reasonably comprehensive utilization of multi feature of image is a crucial problem. In this paper, a weighted fusion using color and texture features of image retrieval algorithm, in the algorithm into the genetic algorithm to adaptively determine their optimal weight coefficient. Key Technologies of Genetic Algorithm Genetic algorithm (GA) that is a simulation of the process of biological evolution is a kind of method that based on the principle of survival of the fittest to solve practical problems. In genetic algorithm, a potential solution set is constructed into an initial population, in which each individual is gene encoded. After the initial population is determined, it is necessary to simulate the process of biological evolution. In the evolution process, a new generation of population gradually closed to the optimal solution is created through the population selection, crossover and mutation operation, produced, finally the practical problem can be solved. The key technology of genetic algorithm [9] can be summed up as a seven-tuple: ) , , , , , , ( T s f C GA ψ τ φ = (1) Among them, C denotes individual encoding mode, f on behalf of fitness function, s represents the initial population, φ denotes selection operator, τ represents crossover operator, ψ represents mutation operator, and T denotes the termination condition of algorithm. Next, we will introduce the several key technologies one by one. A. Coding Mode C Coding mode is the first step and the base in the genetic algorithm. Only biological genetic coding determined, biological traits can be identified. Binary coding, is one of commonly used coding methods. Binary code is relatively simple, it adopts a binary 0,1 string to represent an individual, 1 denotes having certain characteristics, and 0 does not having. B. Fitness Function f In the image retrieval process based on GA, the main basis for judgment is the fitness value. Therefore, choose appropriate fitness function is very important. It not only affects the convergence speed of genetic algorithm, but also influences the ability to find the global optimal solution. In the proposed algorithm, the fitness function of each individual is set:

Adaptive Adjustment of Feature Weight Coefficients Based on Genetic Algorithm in Image Retrieval